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Some years ago I stumbled over this interesting article about C being the most effective of programming language and one making the least false promises. Essentially Damien Katz argues that the simplicity of C and its flaws lead to simple, fast and easy to reason about code.

C is the total package. It is the only language that’s highly productive, extremely fast, has great tooling everywhere, a large community, a highly professional culture, and is truly honest about its tradeoffs.

-Damien Katz about the C Programming language

I am Java developer most of the time but I also have reasonable experience in C, C++, C#, Groovy and Python and some other languages to a lesser extent. Damien’s article really made me think for quite some time about the languages I have been using. I think he is right in many aspects and has really good points about the tools and communities around the languages.

After quite some thought I do not completely agree with him.

My take on C

At a time I really liked the simplicity of C. I wrote gtk2hack in my spare time as an exercise and definitely see interoperability and a quick “build, run, debug”-cycle as big wins for C. On the other hand I think while it has a place in hardware and systems programming many other applications have completely different requirements.

A standardized ABI means nothing to me if I am writing a service with a REST/JSON interface or a standalone GUI application.

Portability means nothing to me if the target system(s) are well defined and/or covered by the runtime of choice.

Startup times mean nothing to me if the system is only started once every few months and development is still fast because of hot-code replacement or other means.

etc.

But I am really missing more powerful abstractions and better error handling or ressource management features. Data structures and memory management are a lot more painful than in other languages. And this is not (only) about garbage collection!

Especially C++ is making big steps in the right direction in the last few years. Each new standard release provides additional features making code more readable and less error prone. With zero cost abstractions at the core of language evolution and the secondary aim of ease of use I really like what will come to C++ in the future. And it has a very professional community, too.

Aims for the C++11 effort:

Make C++ a better language for systems programming and library building

Make C++ easier to teach and learn

-Bjarne Stroustup, A Tour of C++

What we can learn from C

Instead of looking down at C and pointing at its flaws we should look at its strengths and our own weaknesses/flaws. All languages and environments I have used to date have their own set of annoyances and gotchas.

Java people should try building simple things and having a keen eye on dependencies especially because the eco system is so rich and crowded. Also take care of ressource management – the garbage collector is only half the deal.

Scala and C++ people should take a look at ABI stability and interoperability in general. Their compile times and “build, run, debug”-cycle has much room for improvement to say the least.

C# may look at simplicity instead of wildly adding new features creating a language without opinion. A plethora of ways implementing the same stuff. Either you ban features or you have to know them all to understand code in a larger project.

Conclusion

My personal answer to the title of this blog: Yes, they make false promises. But they have a lot to offer, too.

So do not settle with the status quo of your language environment or code style of choice. Try to maintain an objective perspective and be aware of the weaknesses of the tools you are using. Most platforms improve over time and sometimes you have to re-evaluate your opinion regarding some technology.

I prefer C++ to C for some time now and did not look back yet. But I also constantly try different languages, platforms and frameworks and try to maintain a balanced view. There are often good reasons to choose one over the other for a particular project.

Almost two decades ago one of the programming books was published that had a big impact on my thinking as a software engineer: the pragmatic programmer. Most of the tips and practices are still fundamental to my work. If you haven’t read it, give it a try.
Over the years I refined some practices and began to get a renewed focus on additional topics. One of the most important topics of the original tips and of my profession is to care and think about my craft.
In this post I collected a list of tips and practices which helped and still help me in my daily work.

Think about production

Since I develop software to be used, thinking early about the production environment is key.

Deploy as early as possible
Deployment should be a non event. Create an automatic deployment process to keep it that way and deploy as early as possible to remove the risk from unpleasant surprises.

Master should always be deployable
Whether you use master or another branch, you need a branch which could always be deployed without risk.

Self containment
Package (as many as possible of) your dependencies into your deployment. Keep the surprises of missing repositories or dependencies to a minimum of none.

Use real data in development
Real data has characteristics, gaps and inconsistencies you cannot imagine. During development use real data to experience problems before they get into production.

No data loss
Deploying should not result in a loss of data. Your application should shutdown gracefully. Often deployment deletes the directory or uses a fresh place. No files or state in memory should be used as persistence by the application. Applications should be stateless processes.

Rollback
If anything goes wrong or the new deployed application has a serious bug you need to revert it to the last version. Make rollback a requirement.

No user interruption
Users work with your application. Even if they do not lose data or their current work when you deploy, they do not like surprises.

Separate one off tasks
Software should be running and available to the user. Do not delay startup with one off admin tasks like migration, cache warm-up or search index creation. Make your application start in seconds.

Manage your runs
Problems, performance degradation and bugs should be visible. Monitor your key metrics, log important things and detect problems in the application’s data. Make it easy to combine, search and graph your recordings.

Make it easy to reproduce
When a bug occurs or your user has a problem, you need to follow the steps how the system arrived at its current state. Store their actions so that they can be easily replayed.

Think about users

Software is used by people. In order to craft successful applications I have to consider what these people need.

No requirements, just jobs
Users use the software to get stuff done. Features and requirements confuse solutions with problems. Understand in what situation the user is and what he needs to get to his goal. This is the job you need to support.

Work with the user
In order to help the user with your software I need to relate to his situation. Observing, listening, talking to and working along him helps you see his struggles and where software can help.

Speak their language
Users think and speak in their domain. Not in the domain of software. When you want to help them, you and the user interface of your software needs to speak like a user, not like a software.

Value does not come from effort
The most important things your software does are not the ones which need the most effort. Your users value things which help them the most. Find these.

Think about modeling

A model is at the core of your software. Every system has a state. How you divide and manage this state is crucial to evolving and understanding your creation.

Use the language of the domain
In your core you model concepts from the user’s domain. Name them accordingly and reasoning about them and with the users is easier.

Everything has one purpose
Divide your model by the purpose of its parts.

Separate read from write
You won’t get the model right from the start. It is easier to evolve the model if read and write operations have their own model. You can even have different read models for different use cases. (see also CQRS and Turning the database inside out)

Different parts evolve at different speeds
Not all parts of a model are equal. Some stand still, some change frequently. Some are specified, about some others you learn step by step. Some need to be constant, some need to be experimented with. Separating parts by its changing speed will help you deal with change.

Favor immutability
State is hard. State is needed. Isolating state helps you understand a running system. Isolating state helps you remove coupling.

Keep it small
Reasoning about a large system is complicated. Keep effects at bay and models small. Separating and isolating things gives you a chance to overview the whole system.

Think about approaches

Getting to all this is a journey.

When thinking use all three dimensions
Constraining yourself to a computer screen for thinking deprives you of one of your best thinking tools: spatial reasoning. Use whiteboards, walls, paper and more to remove the boundaries from your thoughts.

Crazy 8
Usually you think in your old ways. Getting out of your (mental) box is not easy. Crazy 8 is a method to create 8 solutions (sketches for UI) in a very short time frame.

Suspend judgement
As a programmer you are fast to assess proposals and solutions. Don’t do that. Learn to suspend your judgement. Some good ideas are not so obvious, you will kill them with your judgement.

Get out
Thinking long and hard about a problem can put you into blindfold mode. After stating the problem, get out. Take a walk. Do not actively think or talk about the problem. This simulates the “shower effect”: getting the best ideas when you do not actively think about the problem.

Assume nothing
Assumptions bear risks. They can make your project fail. Approach your project with what is certain. Choose your direction to explore and find your assumptions. Each assumption is an obstacle, an question that needs an answer. Ask your users. Design hypotheses and experiments to proof them. (see From agile to UX for a detailed approach)

Pre-mortem
Another way to find blind spots in your thinking is to frame for failure. Construct a scenario in which your project is failed. Then reason about what made it fail. Where are your biggest risks? (see How to map your fears for details)

MVA – Minimum, valuable action
Every step, every experiment should be as lightweight as possible. Do not craft a beautiful prototype if a sketch would suffice. Choose the most efficient method to get further to your goal.

Put it into a time box
When you need to experiment, constrain it. Define a time in which you want to have an answer. You do not need to go the whole way to get an impression.

We want to create a couple of instances with the names “a”, “b” and “c”. At the beginning of the program we want to start each module, and at the end of the program we want to stop each module. For the creation of the instances we use a map() function call on the names array:

Each module is created twice, and the creation calls are interleaved with the start() and stop() calls.

What has happened?

The answer is that .NET’s Select() method does lazy evaluation. It does not return a new list with the mapped elements. It returns an IEnumerable instead, which evaluates each mapping operation only when needed. This is a very useful concept. It allows for the chaining of multiple operations without creating an intermediate list each time. It also allows for operations on infinite sequences.

But in our case it’s not what we want. The stopped instances are not the same as the started instances.

How can we fix it?

By appending a .ToList() call after the .Select() call:

var modules = names.Select(
name => new Module(name)).ToList();

Now the IEnumerable gets evaluated and collected into a list before the assignment to the modules variable.

So be aware of whether your programming language or framework uses lazy or eager evaluation for functional collection operations to avoid running into subtle bugs. Other examples of tools based on the concept of lazy evaluation are the Java stream API or the Haskell programming language. Some languages support both, for example Ruby since version 2.0: